Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.0K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
Aggregates Classification01:29

Aggregates Classification

317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K
Bias01:22

Bias

4.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

179
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
179

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Geometric brain signatures of Alzheimer's disease progression and subtypes.

medRxiv : the preprint server for health sciences·2026
Same author

Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prexdiction with Multimodal Biomedical Data.

ArXiv·2026
Same author

Integrating Social Determinants of Health in a Multi-Modal Deep Clustering Survival Model for Injury-Risk in Alzheimer's and Related Dementia Patients.

Proceedings of machine learning research·2026
Same author

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach.

Proceedings of machine learning research·2026
Same author

IRIS: Interpretable Risk Clustering Intelligence for Survival Analysis.

Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data·2026
Same author

Multi-Modal Deep Clustering Survival Machines for Alzheimer's Disease Subtype Discovery.

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision·2026
Same journal

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift.

Proceedings of machine learning research·2026
Same journal

Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video.

Proceedings of machine learning research·2026
Same journal

Perspective: Machine Learning for Health Should Consider Social Drivers of Health.

Proceedings of machine learning research·2026
Same journal

Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression.

Proceedings of machine learning research·2026
Same journal

Does Domain-Specific Retrieval Augmented Generation Help LLMs Answer Consumer Health Questions?

Proceedings of machine learning research·2026
Same journal

Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential.

Proceedings of machine learning research·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

公平意识的班级对多个子组的不平衡学习

Davoud Ataee Tarzanagh1, Bojian Hou1, Boning Tong1

  • 1University of Pennsylvania.

Proceedings of machine learning research
|April 11, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的贝叶斯优化框架,以改善过度参数化的模型中的概括性,特别是不平衡的数据. 新的三级方法提高了少数群体的分类和公平性.

更多相关视频

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

相关实验视频

Last Updated: Jun 28, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 优化理论 优化理论

背景情况:

  • 过度参数化的模型难以概括,特别是在具有不平衡子组和每个子组数据有限的场景中.
  • 现有的方法往往无法同时充分解决公平性和泛化问题,特别是在少数群体阶层.

研究的目的:

  • 开发一种新的基于贝叶斯的优化框架,以增强面临不平衡子组的超参数化模型中的概括性.
  • 在有限的样本设置中,提高少数群体类别的分类准确性和公平性.

主要方法:

  • 一个三级优化框架,包含在小数据集上训练的本地预测器.
  • 在中低层集成一个公平和阶级平衡的预测指标.
  • 对于少数类点的敏度意识最小化和基于上层验证损失的动态损失调整.

主要成果:

  • 理论分析表明加强了分类和公平性概括,在概括界限上有潜在的改进.
  • 经验结果表明,与当前最先进的方法相比,性能优越.
  • 该框架有效地解决了不平衡的子组和有限的样本所带来的挑战.

结论:

  • 拟议的三级贝叶斯优化框架在实现过度参数化的模型的强大概括方面取得了重大进展.
  • 该方法成功地平衡了分类性能和公平性,特别是在代表性不足的群体.
  • 该框架为开发更公平,更有效的机器学习模型提供了一个有希望的方向.